{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using DataFrames\n",
    "using Gadfly\n",
    "using Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>technique</th><th>score</th><th>num_genes</th><th>coverage</th><th>representation</th><th>moi</th><th>seq_depth</th><th>bottleneck_representation</th><th>num_bottlenecks</th><th>noise</th><th>run_idx</th><th>crisprtype</th></tr><tr><th>1</th><td>snr</td><td>2.0047964659806596</td><td>500.0</td><td>5.0</td><td>10.0</td><td>0.25</td><td>10.0</td><td>10.0</td><td>1.0</td><td>0.01</td><td>1.0</td><td>CRISPRi</td></tr><tr><th>2</th><td>snr</td><td>2.5960858933745596</td><td>500.0</td><td>5.0</td><td>10.0</td><td>0.25</td><td>10.0</td><td>10.0</td><td>1.0</td><td>0.01</td><td>2.0</td><td>CRISPRi</td></tr><tr><th>3</th><td>snr</td><td>1.673750994061914</td><td>500.0</td><td>5.0</td><td>10.0</td><td>0.25</td><td>10.0</td><td>10.0</td><td>1.0</td><td>0.01</td><td>3.0</td><td>CRISPRi</td></tr><tr><th>4</th><td>snr</td><td>1.7903205387627041</td><td>500.0</td><td>5.0</td><td>10.0</td><td>0.25</td><td>10.0</td><td>10.0</td><td>1.0</td><td>0.01</td><td>4.0</td><td>CRISPRi</td></tr><tr><th>5</th><td>snr</td><td>1.8536620530435448</td><td>500.0</td><td>5.0</td><td>10.0</td><td>0.25</td><td>10.0</td><td>10.0</td><td>1.0</td><td>0.01</td><td>5.0</td><td>CRISPRi</td></tr><tr><th>6</th><td>snr</td><td>1.2260337830871457</td><td>500.0</td><td>5.0</td><td>10.0</td><td>0.25</td><td>10.0</td><td>10.0</td><td>1.0</td><td>0.01</td><td>6.0</td><td>CRISPRi</td></tr></table>"
      ],
      "text/plain": [
       "6×12 DataFrames.DataFrame\n",
       "│ Row │ technique │ score   │ num_genes │ coverage │ representation │ moi  │\n",
       "├─────┼───────────┼─────────┼───────────┼──────────┼────────────────┼──────┤\n",
       "│ 1   │ \"snr\"     │ 2.0048  │ 500.0     │ 5.0      │ 10.0           │ 0.25 │\n",
       "│ 2   │ \"snr\"     │ 2.59609 │ 500.0     │ 5.0      │ 10.0           │ 0.25 │\n",
       "│ 3   │ \"snr\"     │ 1.67375 │ 500.0     │ 5.0      │ 10.0           │ 0.25 │\n",
       "│ 4   │ \"snr\"     │ 1.79032 │ 500.0     │ 5.0      │ 10.0           │ 0.25 │\n",
       "│ 5   │ \"snr\"     │ 1.85366 │ 500.0     │ 5.0      │ 10.0           │ 0.25 │\n",
       "│ 6   │ \"snr\"     │ 1.22603 │ 500.0     │ 5.0      │ 10.0           │ 0.25 │\n",
       "\n",
       "│ Row │ seq_depth │ bottleneck_representation │ num_bottlenecks │ noise │\n",
       "├─────┼───────────┼───────────────────────────┼─────────────────┼───────┤\n",
       "│ 1   │ 10.0      │ 10.0                      │ 1.0             │ 0.01  │\n",
       "│ 2   │ 10.0      │ 10.0                      │ 1.0             │ 0.01  │\n",
       "│ 3   │ 10.0      │ 10.0                      │ 1.0             │ 0.01  │\n",
       "│ 4   │ 10.0      │ 10.0                      │ 1.0             │ 0.01  │\n",
       "│ 5   │ 10.0      │ 10.0                      │ 1.0             │ 0.01  │\n",
       "│ 6   │ 10.0      │ 10.0                      │ 1.0             │ 0.01  │\n",
       "\n",
       "│ Row │ run_idx │ crisprtype │\n",
       "├─────┼─────────┼────────────┤\n",
       "│ 1   │ 1.0     │ \"CRISPRi\"  │\n",
       "│ 2   │ 2.0     │ \"CRISPRi\"  │\n",
       "│ 3   │ 3.0     │ \"CRISPRi\"  │\n",
       "│ 4   │ 4.0     │ \"CRISPRi\"  │\n",
       "│ 5   │ 5.0     │ \"CRISPRi\"  │\n",
       "│ 6   │ 6.0     │ \"CRISPRi\"  │"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = readtable(\"data/bigiron_growth_snr_49b2f59_clean.csv\")\n",
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>technique</th><th>score</th><th>num_genes</th><th>coverage</th><th>moi</th><th>num_bottlenecks</th><th>noise</th><th>run_idx</th><th>crisprtype</th><th>rep_lvl</th></tr><tr><th>1</th><td>snr</td><td>2.0047964659806596</td><td>500.0</td><td>5.0</td><td>0.25</td><td>1.0</td><td>0.01</td><td>1.0</td><td>CRISPRi</td><td>x10</td></tr><tr><th>2</th><td>snr</td><td>2.5960858933745596</td><td>500.0</td><td>5.0</td><td>0.25</td><td>1.0</td><td>0.01</td><td>2.0</td><td>CRISPRi</td><td>x10</td></tr><tr><th>3</th><td>snr</td><td>1.673750994061914</td><td>500.0</td><td>5.0</td><td>0.25</td><td>1.0</td><td>0.01</td><td>3.0</td><td>CRISPRi</td><td>x10</td></tr><tr><th>4</th><td>snr</td><td>1.7903205387627041</td><td>500.0</td><td>5.0</td><td>0.25</td><td>1.0</td><td>0.01</td><td>4.0</td><td>CRISPRi</td><td>x10</td></tr><tr><th>5</th><td>snr</td><td>1.8536620530435448</td><td>500.0</td><td>5.0</td><td>0.25</td><td>1.0</td><td>0.01</td><td>5.0</td><td>CRISPRi</td><td>x10</td></tr><tr><th>6</th><td>snr</td><td>1.2260337830871457</td><td>500.0</td><td>5.0</td><td>0.25</td><td>1.0</td><td>0.01</td><td>6.0</td><td>CRISPRi</td><td>x10</td></tr></table>"
      ],
      "text/plain": [
       "6×10 DataFrames.DataFrame\n",
       "│ Row │ technique │ score   │ num_genes │ coverage │ moi  │ num_bottlenecks │\n",
       "├─────┼───────────┼─────────┼───────────┼──────────┼──────┼─────────────────┤\n",
       "│ 1   │ \"snr\"     │ 2.0048  │ 500.0     │ 5.0      │ 0.25 │ 1.0             │\n",
       "│ 2   │ \"snr\"     │ 2.59609 │ 500.0     │ 5.0      │ 0.25 │ 1.0             │\n",
       "│ 3   │ \"snr\"     │ 1.67375 │ 500.0     │ 5.0      │ 0.25 │ 1.0             │\n",
       "│ 4   │ \"snr\"     │ 1.79032 │ 500.0     │ 5.0      │ 0.25 │ 1.0             │\n",
       "│ 5   │ \"snr\"     │ 1.85366 │ 500.0     │ 5.0      │ 0.25 │ 1.0             │\n",
       "│ 6   │ \"snr\"     │ 1.22603 │ 500.0     │ 5.0      │ 0.25 │ 1.0             │\n",
       "\n",
       "│ Row │ noise │ run_idx │ crisprtype │ rep_lvl │\n",
       "├─────┼───────┼─────────┼────────────┼─────────┤\n",
       "│ 1   │ 0.01  │ 1.0     │ \"CRISPRi\"  │ x10     │\n",
       "│ 2   │ 0.01  │ 2.0     │ \"CRISPRi\"  │ x10     │\n",
       "│ 3   │ 0.01  │ 3.0     │ \"CRISPRi\"  │ x10     │\n",
       "│ 4   │ 0.01  │ 4.0     │ \"CRISPRi\"  │ x10     │\n",
       "│ 5   │ 0.01  │ 5.0     │ \"CRISPRi\"  │ x10     │\n",
       "│ 6   │ 0.01  │ 6.0     │ \"CRISPRi\"  │ x10     │"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:rep_lvl] = :none\n",
    "for (rep_amt, noise_lvl) in zip([(10, 10, 10), (100, 100, 100), (1000, 1000, 1000)], [:x10, :x100, :x1000])\n",
    "    selection = [\n",
    "        (:representation, rep_amt[1]),\n",
    "        (:bottleneck_representation, rep_amt[2]), \n",
    "        (:seq_depth, rep_amt[3])\n",
    "    ]\n",
    "    df[vec(all(hcat([(df[item[1]] .== item[2]) for item in selection]...), 2)), :rep_lvl] = noise_lvl\n",
    "end\n",
    "delete!(df, [:representation, :seq_depth, :bottleneck_representation])\n",
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>num_bottlenecks</th><th>crisprtype</th><th>rep_lvl</th><th>technique</th><th>std_score</th><th>mean_score</th><th>score_max</th><th>score_min</th><th>n</th></tr><tr><th>1</th><td>1.0</td><td>CRISPRKO</td><td>x10</td><td>noise</td><td>0.06515262891235554</td><td>0.7008299270857031</td><td>0.7344486836044786</td><td>0.6672111705669277</td><td>25</td></tr><tr><th>2</th><td>1.0</td><td>CRISPRKO</td><td>x10</td><td>signal</td><td>0.23373782689616676</td><td>0.8692242210119194</td><td>0.9898329396903415</td><td>0.7486155023334974</td><td>25</td></tr><tr><th>3</th><td>1.0</td><td>CRISPRKO</td><td>x10</td><td>snr</td><td>0.40498352106942564</td><td>1.2583363739684486</td><td>1.4673078708402723</td><td>1.0493648770966248</td><td>25</td></tr><tr><th>4</th><td>1.0</td><td>CRISPRKO</td><td>x100</td><td>noise</td><td>0.025883157061396714</td><td>0.31201608296480304</td><td>0.32537179200848376</td><td>0.2986603739211223</td><td>25</td></tr><tr><th>5</th><td>1.0</td><td>CRISPRKO</td><td>x100</td><td>signal</td><td>0.15841055492558598</td><td>0.5768083010799948</td><td>0.6585481474215972</td><td>0.49506845473839245</td><td>25</td></tr><tr><th>6</th><td>1.0</td><td>CRISPRKO</td><td>x100</td><td>snr</td><td>0.5054823278678255</td><td>1.8582379059928502</td><td>2.119066787172648</td><td>1.5974090248130524</td><td>25</td></tr></table>"
      ],
      "text/plain": [
       "6×9 DataFrames.DataFrame\n",
       "│ Row │ num_bottlenecks │ crisprtype │ rep_lvl │ technique │ std_score │\n",
       "├─────┼─────────────────┼────────────┼─────────┼───────────┼───────────┤\n",
       "│ 1   │ 1.0             │ \"CRISPRKO\" │ x10     │ \"noise\"   │ 0.0651526 │\n",
       "│ 2   │ 1.0             │ \"CRISPRKO\" │ x10     │ \"signal\"  │ 0.233738  │\n",
       "│ 3   │ 1.0             │ \"CRISPRKO\" │ x10     │ \"snr\"     │ 0.404984  │\n",
       "│ 4   │ 1.0             │ \"CRISPRKO\" │ x100    │ \"noise\"   │ 0.0258832 │\n",
       "│ 5   │ 1.0             │ \"CRISPRKO\" │ x100    │ \"signal\"  │ 0.158411  │\n",
       "│ 6   │ 1.0             │ \"CRISPRKO\" │ x100    │ \"snr\"     │ 0.505482  │\n",
       "\n",
       "│ Row │ mean_score │ score_max │ score_min │ n  │\n",
       "├─────┼────────────┼───────────┼───────────┼────┤\n",
       "│ 1   │ 0.70083    │ 0.734449  │ 0.667211  │ 25 │\n",
       "│ 2   │ 0.869224   │ 0.989833  │ 0.748616  │ 25 │\n",
       "│ 3   │ 1.25834    │ 1.46731   │ 1.04936   │ 25 │\n",
       "│ 4   │ 0.312016   │ 0.325372  │ 0.29866   │ 25 │\n",
       "│ 5   │ 0.576808   │ 0.658548  │ 0.495068  │ 25 │\n",
       "│ 6   │ 1.85824    │ 2.11907   │ 1.59741   │ 25 │"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_info = by(df, [:num_bottlenecks, :crisprtype, :rep_lvl, :technique]) do grouped_df\n",
    "    grouped_df = grouped_df[1:25, :]\n",
    "    n = size(grouped_df, 1)\n",
    "    mean_score = mean(grouped_df[:score])\n",
    "    std_score = std(grouped_df[:score])\n",
    "    conf_int = 2.58 * std_score./sqrt(n)\n",
    "    DataFrame(\n",
    "        std_score = std_score,\n",
    "        mean_score = mean_score,\n",
    "        score_max = mean_score + conf_int,\n",
    "        score_min = mean_score - conf_int,\n",
    "        n = n\n",
    "    )\n",
    "end\n",
    "head(grouped_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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   "source": [
    "lmhlt(x, y) = x == :x10 && y != :x10 || x == :x100 && y == :x1000\n",
    "sort!(grouped_info, lt=lmhlt)\n",
    "\n",
    "data = grouped_info[grouped_info[:crisprtype] .== \"CRISPRi\", :]\n",
    "\n",
    "p = plot(data, \n",
    "x=:num_bottlenecks,\n",
    "y=:mean_score,\n",
    "ymax=:score_max,\n",
    "ymin=:score_min,\n",
    "color=:technique,\n",
    "ygroup=:rep_lvl,\n",
    "Geom.subplot_grid(\n",
    "Geom.line,\n",
    "Geom.ribbon,\n",
    "# Scale.y_log10,\n",
    "Coord.cartesian(ymin=0, xmin=1),\n",
    "free_y_axis=true,\n",
    "Guide.xticks(ticks=[1,5,10,15,20])\n",
    "),\n",
    "Guide.title(\"Growth screens also exhibit a signal/noise tradeoff\\n(all decreasing, CRISPRi)\"),\n",
    ")\n",
    "draw(SVG(\"plots/growth_snr_tradeoff_crispri.svg\", 15cm, 20cm), p)\n",
    "draw(PNG(\"plots/growth_snr_tradeoff_crispri.png\", 15cm, 20cm, dpi=300), p)\n",
    "draw(SVG(10cm, 20cm), p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "Compose.SVG(100.0mm,200.0mm,IOBuffer(data=UInt8[...], readable=true, writable=true, seekable=true, append=false, size=19211, maxsize=Inf, ptr=19212, mark=-1),nothing,\"img-7dea54ef\",0,Compose.SVGPropertyFrame[],Dict{Type{T},Union{Compose.Property{P<:Compose.PropertyPrimitive},Void}}(Compose.Property{Compose.FillPrimitive}=>nothing,Compose.Property{Compose.JSCallPrimitive}=>nothing,Compose.Property{Compose.SVGClassPrimitive}=>nothing,Compose.Property{Compose.StrokePrimitive}=>nothing),Dict{Compose.ClipPrimitive{P<:NTuple{N,Measures.Measure}},ASCIIString}(Compose.ClipPrimitive{Tuple{Measures.Length{:mm,Float64},Measures.Length{:mm,Float64}}}([(12.51193033854166mm,17.180034722222217mm),(78.76429850260416mm,17.180034722222217mm),(78.76429850260416mm,185.48806966145833mm),(12.51193033854166mm,185.48806966145833mm)])=>\"img-7dea54ef-14\",Compose.ClipPrimitive{Tuple{Measures.Length{:mm,Float64},Measures.Length{:mm,Float64}}}([(24.36928168402777mm,19.180034722222217mm),(76.76429850260416mm,19.180034722222217mm),(76.76429850260416mm,69.93311360677082mm),(24.36928168402777mm,69.93311360677082mm)])=>\"img-7dea54ef-79\",Compose.ClipPrimitive{Tuple{Measures.Length{:mm,Float64},Measures.Length{:mm,Float64}}}([(24.36928168402777mm,128.68619249131945mm),(76.76429850260416mm,128.68619249131945mm),(76.76429850260416mm,181.43927137586803mm),(24.36928168402777mm,181.43927137586803mm)])=>\"img-7dea54ef-23\",Compose.ClipPrimitive{Tuple{Measures.Length{:mm,Float64},Measures.Length{:mm,Float64}}}([(24.36928168402777mm,73.93311360677082mm),(76.76429850260416mm,73.93311360677082mm),(76.76429850260416mm,124.68619249131942mm),(24.36928168402777mm,124.68619249131942mm)])=>\"img-7dea54ef-52\"),Tuple{Compose.FormPrimitive,ASCIIString}[],Set{AbstractString}(),true,false,nothing,true,\"img-7dea54ef-113\",false,113,AbstractString[\"/Users/tamasnagy/.julia/v0.4/Gadfly/src/gadfly.js\"],Tuple{AbstractString,AbstractString}[(\"Snap.svg\",\"Snap\"),(\"Gadfly\",\"Gadfly\")],AbstractString[\"fig.select(\\\"#img-7dea54ef-3\\\")\\n   .drag(function() {}, function() {}, function() {});\",\"fig.select(\\\"#img-7dea54ef-5\\\")\\n   .data(\\\"color_class\\\", \\\"color_noise\\\")\\n.click(Gadfly.colorkey_swatch_click)\\n;\",\"fig.select(\\\"#img-7dea54ef-6\\\")\\n   .data(\\\"color_class\\\", \\\"color_signal\\\")\\n.click(Gadfly.colorkey_swatch_click)\\n;\",\"fig.select(\\\"#img-7dea54ef-7\\\")\\n   .data(\\\"color_class\\\", \\\"color_snr\\\")\\n.click(Gadfly.colorkey_swatch_click)\\n;\",\"fig.select(\\\"#img-7dea54ef-9\\\")\\n   .data(\\\"color_class\\\", \\\"color_noise\\\")\\n.click(Gadfly.colorkey_swatch_click)\\n;\",\"fig.select(\\\"#img-7dea54ef-10\\\")\\n   .data(\\\"color_class\\\", \\\"color_signal\\\")\\n.click(Gadfly.colorkey_swatch_click)\\n;\",\"fig.select(\\\"#img-7dea54ef-11\\\")\\n   .data(\\\"color_class\\\", \\\"color_snr\\\")\\n.click(Gadfly.colorkey_swatch_click)\\n;\",\"fig.select(\\\"#img-7dea54ef-15\\\")\\n   .init_gadfly();\",\"fig.select(\\\"#img-7dea54ef-24\\\")\\n   .init_gadfly();\",\"fig.select(\\\"#img-7dea54ef-53\\\")\\n   .init_gadfly();\",\"fig.select(\\\"#img-7dea54ef-80\\\")\\n   .init_gadfly();\"],false,:none)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lmhlt(x, y) = x == :x10 && y != :x10 || x == :x100 && y == :x1000\n",
    "sort!(grouped_info, lt=lmhlt)\n",
    "\n",
    "data = grouped_info[grouped_info[:crisprtype] .== \"CRISPRKO\", :]\n",
    "\n",
    "p = plot(data, \n",
    "x=:num_bottlenecks,\n",
    "y=:mean_score,\n",
    "ymax=:score_max,\n",
    "ymin=:score_min,\n",
    "color=:technique,\n",
    "ygroup=:rep_lvl,\n",
    "Geom.subplot_grid(\n",
    "Geom.line,\n",
    "Geom.ribbon,\n",
    "# Scale.y_log10,\n",
    "Coord.cartesian(ymin=0, xmin=1),\n",
    "free_y_axis=true,\n",
    "Guide.xticks(ticks=[1,5,10,15,20])\n",
    "),\n",
    "Guide.title(\"Growth screens also exhibit a signal/noise tradeoff\\n(all decreasing, CRISPRn)\"),\n",
    ")\n",
    "draw(SVG(\"plots/growth_snr_tradeoff_crisprn.svg\", 15cm, 20cm), p)\n",
    "draw(PNG(\"plots/growth_snr_tradeoff_crisprn.png\", 15cm, 20cm, dpi=300), p)\n",
    "draw(SVG(10cm, 20cm), p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.4.6",
   "language": "julia",
   "name": "julia-0.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
